Chest X-Ray Image Analysis with Combining 2D and 1D Convolutional Neural Network Based Classifier for Rapid Cardiomegaly Screening

Jian Xing Wu, Ching Chou Pai, Chung Dann Kan, Pi Yun Chen, Wei Ling Chen, Chia Hung Lin

研究成果: Article同行評審

摘要

Cardiomegaly is an asymptomatic disease. Symptoms, such as palpitations, chest tightness, and shortness of breath, may be the early indications of cardiac hypertrophy, which can be divided into cardiac hypertrophy and ventricular enlargement. Their causes and treatment strategies are different. The early detection of cardiomegaly can help to make decisions for administering drugs and surgical treatments. In addition, with regard to problems in manual inspection, such as time consuming and the need for human interpretations and experiences, an assistive tool is required to automatically develop and identify normal heart or enlarged hearts. Therefore, this study proposes the combination of 2D (two dimensional) and 1D (one dimensional) convolutional neural network based classifier for rapid cardiomegaly screening in clinical applications based on chest X-ray (CXR) examinations in frontal posteroanterior view. The 2D and 1D convolutional processes and multilayer connected classification network are used to enhance the original CXR image and to remove unwanted noises to increase accuracy in feature extraction and pattern recognition tasks. The training dataset and testing dataset are collected from the National Institutes of Health CXR image database, which is used to train the classifier and validate the performance of the classifier in a K-fold cross-validation manner. Experimental results indicate the potential performance for rapid cardiomegaly screening with regard to recall (%), precision (%), accuracy (%), and F1 score.

原文English
頁(從 - 到)47824-47836
頁數13
期刊IEEE Access
10
DOIs
出版狀態Published - 2022

All Science Journal Classification (ASJC) codes

  • 電腦科學(全部)
  • 材料科學(全部)
  • 工程 (全部)
  • 電氣與電子工程

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